Virtual learning is becoming the norm for a lot of people and is easily accessible regardless of location. That’s one of the reasons why we’re hosting the ODSC East 2020 Virtual Conference this April 14-17 so anyone can learn data science online.
This 100%-virtual event will contain the same breadth and depth of content and engagement that you can experience at one of our in-person events. Here are a few reasons why virtual training might be right for you:
Why Online Virtual Training?
In these trying times, it’s important to look ahead. The field of data science continues to expand and the demand for individuals with expertise in data, machine learning, and deep learning will continue to grow.
ODSC is hosting our first ever Virtual Conference and live training will be one of the most important aspects of the event. The conference will feature all of the same things you’d get at an in-person event: trainings, networking, career advice, and more.
World-Renowned Speakers & Trainers
This year, we have speakers representing some of the most impactful data science teams in the world, including Google, Microsoft, MIT, Facebook, Amazon, IBM, NVIDIA, and more.
Highlighted Training Sessions
With 55 training sessions, 75 workshops, and over 400 hours of original content, there’s something for everyone. Here are a few talks that we’re excited about:
- Deep Learning (with TensorFlow 2): Dr. Jon Krohn, Chief Data Scientist, Author of Deep Learning Illustrated | Untapt
- Tensorflow is often used for solving deep learning problems and for training and evaluating processes up to the model deployment, making it a popular choice for deep learning enthusiasts.
- Accelerate ML Lifecycle with Kubernetes and Containerized Data Science Tools: Abhinav Joshi (Sr. Principal Marketing Manager) and Tushar Katarki (Sr. Principal Product Manager) | Red Hat
- Gain a better understanding of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers.
- Applying State-of-the-art Natural Language Processing for Personalized Healthcare: David Talby, PhD, CTO | Pacific AI
- More than half of the clinically relevant data in oncology is only found in free-text pathology reports, radiology reports, sequencing reports, and progress notes, meaning any data scientist who wants to be involved in healthcare should be able to do NLP.
- Training and Operationalizing Interpretable Machine Learning Models: Francesca Lazzeri, PhD, Senior ML Scientist | Microsoft
- Check out some common challenges of machine learning model deployment such as choosing the right tools, how to use autoML, and more, and how to address these challenges.
- Smart Technologies in Enhancing Browsing Experiences: Zona Kostic, PhD, Research Fellow: Harvard University
- Physical and virtual searching follow different rules, logic, and procedures, each with their own pros and setbacks. How do you develop applications that seek to intertwine two types of search, such as with augmented reality?
- Introduction to Machine Learning with scikit-learn: Andreas Mueller, PhD, Author, Research Scientist, Core Contributor of scikit-learn | Columbia Data Science Institute
- Machine learning has become an indispensable tool across many areas of research and commercial applications. Learn how to get started by using scikit-learn.
- Machine Learning in R Part I: Penalized Regression and Boosted Trees: Jared Lander, Chief Data Scientist, Author of R for Everyone, Professor | Lander Analytics, Columbia Business School
- Linear regression is the foundation of supervised learning, though it has its limits. During this workshop, we extend regression using penalization for automated variable selection and increased flexibility.
- Machine Learning in R Part II: Using workflows to build an ML optimization pipeline
- Adapting Machine Learning Algorithms to Novel Use Cases: Dr. Kirk Borne, Principal Data Scientist | Booz Allen Hamilton
- By presenting several examples of one of the key aptitudes of successful data science practice, Kirk will present several well-known algorithms that may have been adopted for specific use cases or applied in specific business domains, and then I will show how each one can be adapted to a novel use case that may be less obvious, perhaps producing significantly surprising results in some other domain.
- Kirk’s second talk: Solving the Data Scientist’s Dilemma: the Cold-start Problem with 10+ Machine Learning Examples
What’s online training like?
All training sessions are modular, meaning everything is structured in an easy-to-follow format designed to balance instruction, hands-on training, and then a Q&A session. This ensures that you get the information you need, followed by a practice run and actual implementation of the new code, then a chance to ask questions related to what you just learned.
It’s in the Cloud
By using Jupyter Notebook and Codelab, most training sessions will be on open-source, cloud-based documents to host and share code, visualizations, text, and other important information needed for your training session.
All of these sessions are in real-time, and function as if you’re at a conference in-person. The presenter will walk through everything right then and there.
Each session will have their own Slack channel, too, meant for real-time discussion with other participants and for the Q&A part of each module.
It’s easy to switch between tracks, too. Everything will be viewed in an easy-to-use dashboard, so it’s simple to switch between focus areas. Halfway through the current machine learning session but want to learn more about NLP? You can easily switch to that one and catch the first half on-demand.
On-demand videos will be made available for each session. Need to rewatch one that you participated in live? Had to choose between two talks that you were interested in? These videos will be available for you immediately. The module format will make it easy for you to follow, especially if you only need to refresh your memory for one section of a session.
Materials are all online
Everything will be right there for you in each session. While it’s recommended that you set up as much as possible before each session, such as downloading required programs and familiarizing yourself a bit with the topic, all session materials will be made available to you right there. Any subsequent materials (such as something mentioned in a Q&A session) can be shared via the Slack channel, too.
If virtual training sounds good to you, and you’re ready to learn about the latest topics, frameworks, and tools in data science, then register now for the ODSC East 2020 Virtual Conference this April 14-17. Tickets are 20% off for a limited time, so be sure to grab your ticket now before it’s too late!